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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 1,333 records · Page 74Linked to original sources

bioRxiv2014

poRe: an R package for the visualization and analysis of nanopore sequencing data

MotivationThe Oxford Nanopore MinION device represents a unique sequencing technology. As a mobile sequencing device powered by the USB port of a laptop, the MinION has huge potential applications. To enable these applications, the bioinformatics community will need to design and build a suite of tools specifically for MinION data.\n\nResultsHere we present poRe, a package for the statistical software R that enables users to manipulate, organize, summarise and visualize MinION nanopore sequencing data. As a packge for R, poRe has been tested on both Windows and Linux. Crucially, the Windows version allows users to analyse MinION data on the Windows laptop attached to the device\n\nAvailabilityPre-built R packages for Windows and Linux are available under a BSD license at http://sourceforge.net/projects/rpore/\n\nContactmick.watson@roslin.ed.ac.uk

Bioinformatics↗

bioRxiv2014

NxTrim: optimized trimming of Illumina mate pair reads

Motivation: Mate pair protocols add to the utility of paired-end sequencing by boosting the genomic distance spanned by each pair of reads, potentially allowing larger repeats to be bridged and resolved. The Illumina Nextera Mate Pair (NMP) protocol employs a circularisation-based strategy that leaves behind 38bp adapter sequences which must be computationally removed from the data. While "adapter trimming" is a well-studied area of bioinformatics, existing tools do not fully exploit the particular properties of NMP data and discard more data than is necessary. Results: We present NxTrim, a tool that strives to discard as little sequence as possible from NMP reads. The sequence either side of the adapter site is triaged into "virtual libraries" of mate pairs, paired-end reads and single-ended reads. When combined, these data boost coverage and can substantially improve the de novo assembly of bacterial genomes.

Bioinformatics↗

bioRxiv2014

Benchmarking undedicated cloud computing providers for analysis of genomic datasets.

A major bottleneck in biological discovery is now emerging at the computational level. Cloud computing offers a dynamic means whereby small and medium-sized laboratories can rapidly adjust their computational capacity. We benchmarked two established cloud computing services, Amazon Web Services Elastic MapReduce (EMR) on Amazon EC2 instances and Google Compute Engine (GCE), using publicly available genomic datasets (E.coli CC102 strain and a Han Chinese male genome) and a standard bioinformatic pipeline on a Hadoop-based platform. Wall-clock time for complete assembly differed by 52.9% (95%CI: 27.5-78.2) for E.coli and 53.5% (95%CI: 34.4-72.6) for human genome, with GCE being more efficient than EMR. The cost of running this experiment on EMR and GCE differed significantly, with the costs on EMR being 257.3% (95%CI: 211.5-303.1) and 173.9% (95%CI: 134.6-213.1) more expensive for E.coli and human assemblies respectively. Thus, GCE was found to outperform EMR both in terms of cost and wall-clock time. Our findings confirm that cloud computing is an efficient and potentially cost-effective alternative for analysis of large genomic datasets. In addition to releasing our cost-effectiveness comparison, we present available ready-to-use scripts for establishing Hadoop instances with Ganglia monitoring on EC2 or GCE.

Bioinformatics↗

bioRxiv2014

edgeRun: an R package for sensitive, functionally relevant differential expression discovery using an unconditional exact test

SummaryNext-generation sequencing platforms for measuring digital expression such as RNA-Seq are displacing traditional microarray-based methods in biological experiments. The detection of differentially expressed genes between groups of biological conditions has led to the development of numerous bioinformatics tools, but so far few, exploit the expanded dynamic range afforded by the new technologies. We present edgeRun, an R package that implements an unconditional exact test that is a more powerful version of the exact test in edgeR. This increase in power is especially pronounced for experiments with as few as 2 replicates per condition, for genes with low total expression and with large biological coefficient of variation. In comparison with a panel of other tools, edgeRun consistently captures functionally similar differentially expressed genes.\n\nAvailabilityThe package is freely available under the MIT license from CRAN (http://cran.r-project.org/web/packages/edgeRun)\n\nContactedimont@mail.harvard.edu

Bioinformatics↗

bioRxiv2014

ProbAlign: a re-alignment method for long sequencing reads

The incorrect alignments are a severe problem in variant calling, and remain as a challenge computational issue in Bioinformatics field. Although there have been some methods utilizing the re-alignment approach to tackle the misalignments, a standalone re-alignment tool for long sequencing reads is lacking. Hence, we present a standalone tool to correct the misalignments, called ProbAlign. It can be integrated into the pipelines of not only variant calling but also other genomic applications. We demonstrate the use of re-alignment in two diverse and important genomics fields: variant calling and viral quasispecies reconstruction. First, variant calling results in the Pacific Biosciences SMRT re-sequencing data of NA12878 show that false positives can be reduced by 43.5%, and true positives can be increased by 24.8% averagely, after re-alignment. Second, results in reconstructing a 5-virus-mix show that the viral population can be completely unraveled, and also the estimation of quasispecies frequencies has been improved, after re-alignment. ProbAlign is freely available in the PyroTools toolkit (https://github.com/homopolymer/PyroTools).

Bioinformatics↗

bioRxiv2014

MyGene.info: gene annotation query as a service

Biomedical knowledge is often represented as annotations of biological entities such as genes, genetic variants, diseases, and drugs. For gene annotations, they are fragmented across data repositories like NCBI Entrez, Ensembl, UniProt, and hundreds (or more) of other specialized databases. While the volume and breadth of annotations is valuable, their fragmentation across many data silos is often frustrating and inefficient. Bioinformaticians everywhere must continuously and repetitively engage in data wrangling in an effort to comprehensively integrate knowledge from all these resources, and these uncoordinated efforts represent an enormous duplication of work. We previously released MyGene.info (http://mygene.info) to enable bioinformatics developers to gain programmatic access to gene annotation data through our high-performance web services. This article focuses on the updates to MyGene.info since our last paper (2013 database issue). With the completely re-factored system, MyGene.info now expands the support from the original nine species to over 14K species, covering >17M genes with >50 gene-specific annotation types. Two simple web service endpoints provides high-performance query access to all these aggregated gene annotations. The infrastructure underlying MyGene.info is highly scalable, which offers both high-performance and high-concurrency, and makes MyGene.info particularly suitable for the use cases of real-time applications and analysis pipelines.

Bioinformatics↗

bioRxiv2014

Identifying highly-penetrant disease causal mutations using next generation sequencing: Guide to whole process

Recent technological advances have created challenges for geneticists and a need to adapt to a wide range of new bioinformatics tools and an expanding wealth of publicly available data (e.g. mutation databases, software). This wide range of methods and a diversity of file formats used in sequence analysis is a significant issue, with a considerable amount of time spent before anyone can even attempt to analyse the genetic basis of human disorders. Another point to consider is although many possess just enough knowledge to analyse their data, they do not make full use of the tools and databases that are available and also do not know how their data was created. The primary aim of this review is to document some of the key approaches and provide an analysis schema to make the analysis process more efficient and reliable in the context of discovering highly penetrant causal mutations/genes. This review will also compare the methods used to identify highly penetrant variants when data is obtained from consanguineous individuals as opposed to non-consanguineous; and when Mendelian disorders are analysed as opposed to common-complex disorders.

Bioinformatics↗

bioRxiv2014

Annotating RNA motifs in sequences and alignments

RNA performs a diverse array of important functions across all cellular life. These functions include important roles in translation, building translational machinery and maturing messenger RNA. More recent discoveries include the miRNAs and bacterial sRNAs that regulate gene expression, the thermosensors, riboswitches and other cis-regulatory elements that help prokaryotes sense their environment and eukaryotic piRNAs that suppress transposition. However, there can be a long period between the initial discovery of a RNA and determining its function. We present a bioinformatic approach to characterise RNA motifs, which are the central building blocks of RNA structure. These motifs can, in some instances, provide researchers with functional hypotheses for uncharacterised RNAs. Moreover, we introduce a new profile-based database of RNA motifs - RMfam - and illustrate its application for investigating the evolution and functional characterisation of RNA.\n\nAll the data and scripts associated with this work is available from: https://github.com/ppgardne/RMfam

Bioinformatics↗

bioRxiv2014

CLASS: Accurate and Efficient Splice Variant Annotation from RNA-seq Reads

Next generation sequencing of cellular RNA is making it possible to characterize genes and alternative splicing in unprecedented detail. However, designing bioinformatics tools to capture splicing variation accurately has proven difficult. Current programs find major isoforms of a gene but miss finer splicing variations, or are sensitive but highly imprecise. We present CLASS, a novel open source tool for accurate genome-guided transcriptome assembly from RNA-seq reads. CLASS employs a splice graph to represent a gene and its splice variants, combined with a linear program to determine an accurate set of exons and efficient splice graph-based transcript selection algorithms. When compared against reference programs, CLASS had the best overall accuracy and could detect up to twice as many splicing events with precision similar to the best reference program. Notably, it was the only tool that produced consistently reliable transcript models for a wide range of applications and sequencing strategies, including very large data sets and ribosomal RNA-depleted samples. Lightweight and multi-threaded, CLASS required <3GB RAM and less than one day to analyze a 350 million read set, and is an excellent choice for transcriptomics studies, from clinical RNA sequencing, to alternative splicing analyses, and to the annotation of new genomes.

Bioinformatics↗

bioRxiv2014

SpeedSeq: Ultra-fast personal genome analysis and interpretation

Comprehensive interpretation of human genome sequencing data is a challenging bioinformatic problem that typically requires weeks of analysis, with extensive hands-on expert involvement. This informatics bottleneck inflates genome sequencing costs, poses a computational burden for large-scale projects, and impedes the adoption of time-critical clinical applications such as personalized cancer profiling and newborn disease diagnosis, where the actionable timeframe can measure in hours or days. We developed SpeedSeq, an open-source genome analysis platform that vastly reduces computing time. SpeedSeq accomplishes read alignment, duplicate removal, variant detection and functional annotation of a 50X human genome in <24 hours, even using one low-cost server. SpeedSeq offers competitive or superior performance to current methods for detecting germline and somatic single nucleotide variants (SNVs), indels, and structural variants (SVs) and includes novel functionality for SV genotyping, SV annotation, fusion gene detection, and rapid identification of actionable mutations. SpeedSeq will help bring timely genome analysis into the clinical realm.\n\nAvailability: SpeedSeq is available at https://github.com/cc2qe/speedseq.

Bioinformatics↗

bioRxiv2015

Merging OpenLifeData with SADI services using Galaxy and Docker

Semantic Web technologies have been widely applied in Life Sciences, for example by data providers like OpenLifeData and Web Services frameworks like SADI. The recent OpenLifeData2SADI project offers access to the OpenLifeData data store through SADI services. This paper shows how to merge data from OpenLifeData with other extant SADI services in the Galaxy bioinformatics analysis platform, making semantic data amenable to complex analyses, as a worked example demonstrates. The whole setting is reproducible through a Docker image that includes a pre-configured Galaxy server with data and workflows that constitute the worked example.

Bioinformatics↗

bioRxiv2015

SWORD - a highly efficient protein database search

MotivationProtein database search is one of the fundamental problems in bioinformatics. For decades, it has been explored and solved using different exact and heuristic approaches. However, exponential growth of data in size in recent years has brought significant challenges in improving already existing algorithms. BLASTP has been the most successful tool for protein database search, but is also becoming a bottleneck in many applications. Due to that, many different approaches have been developed to comple-ment or replace BLASTP. In this paper, we present SWORD, an efficient protein database search implementation that runs 3-4 faster than BLASTP in the sensitive mode and up to 18 faster in the fast and less accurate mode and also provides guaranteed optimal alignments for candidate sequences. SWORD is designed to be used in nearly all database search environments, but is especially suitable for large databases. Its sensitivity exceeds that of BLASTP for majority of input datasets.\n\nAvailabilitySword is freely available for download from https://github.com/rvaser/sword\n\nContactrobert.vaser@fer.hr, mile.sikic@fer.hr

Bioinformatics↗

bioRxiv2015

LINKS: Scaffolding genome assemblies with kilobase-long nanopore reads

MotivationOwing to the complexity of the assembly problem, we do not yet have complete genome sequences. The difficulty in assembling reads into finished genomes is exacerbated by sequence repeats and the inability of short reads to capture sufficient genomic information to resolve those problematic regions. Established and emerging long read technologies show great promise in this regard, but their current associated higher error rates typically require computational base correction and/or additional bioinformatics preprocessing before they could be of value. We present LINKS, the Long Interval Nucleotide K-mer Scaffolder algorithm, a solution that makes use of the information in error-rich long reads, without the need for read alignment or base correction. We show how the contiguity of an ABySS E. coli K-12 genome assembly could be increased over five-fold by the use of beta-released Oxford Nanopore Ltd. (ONT) long reads and how LINKS leverages long-range information in S. cerevisiae W303 ONT reads to yield an assembly with less than half the errors of competing applications. Re-scaffolding the colossal white spruce assembly draft (PG29, 20 Gbp) and how LINKS scales to larger genomes is also presented. We expect LINKS to have broad utility in harnessing the potential of long reads in connecting high-quality sequences of small and large genome assembly drafts.\n\nAvailabilityhttp://www.bcgsc.ca/bioinformatics/software/links\n\nContactrwarren@bcgsc.ca

Bioinformatics↗

bioRxiv2015

Bio4j: a high-performance cloud-enabled graph-based data platform

BackgroundNext Generation Sequencing and other high-throughput technologies have brought a revolution to the bioinformatics landscape, by offering sheer amounts of data about previously unaccessible domains in a cheap and scalable way. However, fast, reproducible, and cost-effective data analysis at such scale remains elusive. A key need for achieving it is being able to access and query the vast amount of publicly available data, specially so in the case of knowledge-intensive, semantically rich data: incredibly valuable information about proteins and their functions, genes, pathways, or all sort of biological knowledge encoded in ontologies remains scattered, semantically and physically fragmented.\n\nMethods and ResultsGuided by this, we have designed and developed Bio4j. It aims to offer a platform for the integration of semantically rich biological data using typed graph models. We have modeled and integrated most publicly available data linked with proteins into a set of interdependent graphs. Data querying is possible through a data model aware Domain Specific Language implemented in Java, letting the user write typed graph traversals over the integrated data. A ready to use cloud-based data distribution, based on the Titan graph database engine is provided; generic data import code can also be used for in-house deployment.\n\nConclusionBio4j represents a unique resource for the current Bioinformatician, providing at once a solution for several key problems: data integration; expressive, high performance data access; and a cost-effective scalable cloud deployment model.

Bioinformatics↗

bioRxiv2015

GenOO: A Modern Perl Framework for High Throughput Sequencing analysis

BackgroundHigh throughput sequencing (HTS) has become one of the primary experimental tools used to extract genomic information from biological samples. Bioinformatics tools are continuously being developed for the analysis of HTS data. Beyond some well-defined core analyses, such as quality control or genomic alignment, the consistent development of custom tools and the representation of sequencing data in organized computational structures and entities remains a challenging effort for bioinformaticians.\n\nResultsIn this work, we present GenOO [jee-noo], an open-source; object-oriented (OO) Perl framework specifically developed for the design and implementation of HTS analysis tools. GenOO models biological entities such as genes and transcripts as Perl objects, and includes relevant modules, attributes and methods that allow for the manipulation of high throughput sequencing data. GenOO integrates these elements in a simple and transparent way which allows for the creation of complex analysis pipelines minimizing the overhead for the researcher. GenOO has been designed with flexibility in mind, and has an easily extendable modular structure with minimal requirements for external tools and libraries. As an example of the frameworks capabilities and usability, we present a short and simple walkthrough of a custom use case in HTS analysis.\n\nConclusionsGenOO is a tool of high software quality which can be efficiently used for advanced HTS analyses. It has been used to develop several custom analysis tools, leading to a number of published works. Using GenOO as a core development module can greatly benefit users, by reducing the overhead and complexity of managing HTS data and biological entities at hand.

Bioinformatics↗

bioRxiv2015

Roary: Rapid large-scale prokaryote pan genome analysis

SummaryA typical prokaryote population sequencing study can now consist of hundreds or thousands of isolates. Interrogating these datasets can provide detailed insights into the genetic structure of of prokaryotic genomes. We introduce Roary, a tool that rapidly builds large-scale pan genomes, identifying the core and dispensable accessory genes. Roary makes construction of the pan genome of thousands of prokaryote samples possible on a standard desktop without compromising on the accuracy of results. Using a single CPU Roary can produce a pan genome consisting of 1000 isolates in 4.5 hours using 13 GB of RAM, with further speedups possible using multiple processors.\n\nAvailability and implementationRoary is implemented in Perl and is freely available under an open source GPLv3 license from http://sanger-pathogens.github.io/Roary\n\nContactroary@sanger.ac.uk\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

Bioinformatics↗

bioRxiv2015

Improved Algorithms for Finding Edit Distance Based Motifs

Motif search is an important step in extracting meaningful patterns from biological data. Since the general problem of motif search is intractable, there is a pressing need to develop efficient exact and approximation algorithms to solve this problem. We design novel algorithms for solving the Edit-distance-based Motif Search (EMS) problem: given two integers l, d and n biological strings, find all strings of length l that appear in each input strings with at most d substitutions, insertions and deletions. These algorithms have been evaluated on several challenging instances. Our algorithm solves a moderately hard instance (11, 3) in a couple of minutes and the next difficult instance (14, 3) in a couple of hours whereas the best previously known algorithm, EMS1, solves (11, 3) in a few hours and does not solve (13, 4) even after 3 days. This significant improvement is due to a novel and provably efficient neighborhood generation technique introduced in this paper. This efficient approach can be used in other edit distance based applications in Bioinformatics, such as k-spectrum based sequence error correction algorithms. We also use a trie based data structure to efficiently store the candidate motifs in the neighbourhood and to output the motifs in a sorted order.

Bioinformatics↗

bioRxiv2015

Pyvolve: a flexible Python module for simulating sequences along phylogenies

We introduce Pyvolve, a flexible Python module for simulating genetic data along a phylogeny according to continuous-time Markov models of sequence evolution. Pyvolve is easily incorporated into Python bioinformatics pipelines, and it can simulate sequences according most standard models of nucleotide, amino-acid, and codon sequence evolution. All model parameters are fully customizable. Users can additionally specify custom evolutionary models, with custom rate matrices and/or states to evolve. This flexibility makes Pyvolve a convenient framework not only for simulating sequences under a wide variety of conditions, but also for developing and testing new evolutionary models. Moreover, Pyvolve includes several novel sequence simulation features, including a new rate matrix scaling algorithm and branch-length perturbations. Pyvolve is an open-source project freely available, along with a detailed user-manual and example scripts, under a FreeBSD license from http://github.com/sjspielman/pyvolve.

Bioinformatics↗